System and method for zero-shot object navigation using large language models

This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63/466,212 filed on May 12, 2023, which is hereby incorporated by reference in its entirety.

This disclosure relates generally to object navigation. More specifically, this disclosure relates to a system and method for zero-shot object navigation using large language models.

Object navigation is a task in which an embodied agent must navigate to a specific goal object within an unknown environment. This task can be fundamental to other navigation-based embodied tasks because it enables the agent to interact with the goal object. Such object navigation tasks usually require large-scale training in visual environments with labeled objects.

This disclosure provides a system and method for zero-shot object navigation using large language models.

In a first embodiment, a method includes determining a specified object to locate within a surrounding environment, the surrounding environment comprising multiple candidate rooms and multiple candidate objects. The method also includes causing a robot to capture an image and a depth map of the surrounding environment. The method further includes using a scene understanding model, predicting one or more rooms and one or more objects captured in the image. The method also includes updating a second map of the surrounding environment based on the one or more predicted rooms, the one or more predicted objects, the depth map, and a location of the robot. The method further includes determining a likelihood of the specified object being in each of the candidate rooms and a likelihood of the specified object being near each of the candidate objects using a pre-trained large language model. In addition, the method includes causing the robot to move to a next location for the robot to search for the specified object, based on the determined likelihoods and the second map of the surrounding environment.

In a second embodiment, an electronic device includes at least one processing device configured to determine a specified object to locate within a surrounding environment, the surrounding environment comprising multiple candidate rooms and multiple candidate objects. The at least one processing device is also configured to cause a robot to capture an image and a depth map of the surrounding environment. The at least one processing device is further configured to using a scene understanding model, predict one or more rooms and one or more objects captured in the image. The at least one processing device is also configured to update a second map of the surrounding environment based on the one or more predicted rooms, the one or more predicted objects, the depth map, and a location of the robot. The at least one processing device is further configured to determine a likelihood of the specified object being in each of the candidate rooms and a likelihood of the specified object being near each of the candidate objects using a pre-trained large language model. In addition, the at least one processing device is configured to cause the robot to move to a next location for the robot to search for the specified object, based on the determined likelihoods and the second map of the surrounding environment.

In a third embodiment, a non-transitory machine-readable medium contains instructions that when executed cause at least one processor of an electronic device to determine a specified object to locate within a surrounding environment, the surrounding environment comprising multiple candidate rooms and multiple candidate objects. The non-transitory machine-readable medium also contains instructions that when executed cause the at least one processor to cause a robot to capture an image and a depth map of the surrounding environment. The non-transitory machine-readable medium further contains instructions that when executed cause the at least one processor to using a scene understanding model, predict one or more rooms and one or more objects captured in the image. The non-transitory machine-readable medium also contains instructions that when executed cause the at least one processor to update a second map of the surrounding environment based on the one or more predicted rooms, the one or more predicted objects, the depth map, and a location of the robot. The non-transitory machine-readable medium further contains instructions that when executed cause the at least one processor to determine a likelihood of the specified object being in each of the candidate rooms and a likelihood of the specified object being near each of the candidate objects using a pre-trained large language model. In addition, the non-transitory machine-readable medium contains instructions that when executed cause the at least one processor to cause the robot to move to a next location for the robot to search for the specified object, based on the determined likelihoods and the second map of the surrounding environment.

Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.

Before undertaking the DETAILED DESCRIPTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The terms “transmit,” “receive,” and “communicate,” as well as derivatives thereof, encompass both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrase “associated with,” as well as derivatives thereof, means to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like.

Moreover, various functions described below can be implemented or supported by one or more computer programs, each of which is formed from computer readable program code and embodied in a computer readable medium. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer readable program code. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device.

As used here, terms and phrases such as “have,” “may have,” “include,” or “may include” a feature (like a number, function, operation, or component such as a part) indicate the existence of the feature and do not exclude the existence of other features. Also, as used here, the phrases “A or B,” “at least one of A and/or B,” or “one or more of A and/or B” may include all possible combinations of A and B. For example, “A or B,” “at least one of A and B,” and “at least one of A or B” may indicate all of (1) including at least one A, (2) including at least one B, or (3) including at least one A and at least one B. Further, as used here, the terms “first” and “second” may modify various components regardless of importance and do not limit the components. These terms are only used to distinguish one component from another. For example, a first user device and a second user device may indicate different user devices from each other, regardless of the order or importance of the devices. A first component may be denoted a second component and vice versa without departing from the scope of this disclosure.

It will be understood that, when an element (such as a first element) is referred to as being (operatively or communicatively) “coupled with/to” or “connected with/to” another element (such as a second element), it can be coupled or connected with/to the other element directly or via a third element. In contrast, it will be understood that, when an element (such as a first element) is referred to as being “directly coupled with/to” or “directly connected with/to” another element (such as a second element), no other element (such as a third element) intervenes between the element and the other element.

As used here, the phrase “configured (or set) to” may be interchangeably used with the phrases “suitable for,” “having the capacity to,” “designed to,” “adapted to,” “made to,” or “capable of” depending on the circumstances. The phrase “configured (or set) to” does not essentially mean “specifically designed in hardware to.” Rather, the phrase “configured to” may mean that a device can perform an operation together with another device or parts. For example, the phrase “processor configured (or set) to perform A, B, and C” may mean a generic-purpose processor (such as a CPU or application processor) that may perform the operations by executing one or more software programs stored in a memory device or a dedicated processor (such as an embedded processor) for performing the operations.

The terms and phrases as used here are provided merely to describe some embodiments of this disclosure but not to limit the scope of other embodiments of this disclosure. It is to be understood that the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. All terms and phrases, including technical and scientific terms and phrases, used here have the same meanings as commonly understood by one of ordinary skill in the art to which the embodiments of this disclosure belong. It will be further understood that terms and phrases, such as those defined in commonly-used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined here. In some cases, the terms and phrases defined here may be interpreted to exclude embodiments of this disclosure.

Examples of an “electronic device” according to embodiments of this disclosure may include at least one of a smartphone, a tablet personal computer (PC), a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop computer, a netbook computer, a workstation, a personal digital assistant (PDA), a portable multimedia player (PMP), an MP3 player, a mobile medical device, a camera, or a wearable device (such as smart glasses, a head-mounted device (HMD), electronic clothes, an electronic bracelet, an electronic necklace, an electronic accessory, an electronic tattoo, a smart mirror, or a smart watch). Other examples of an electronic device include a smart home appliance. Examples of the smart home appliance may include at least one of a television, a digital video disc (DVD) player, an audio player, a refrigerator, an air conditioner, a cleaner, an oven, a microwave oven, a washer, a drier, an air cleaner, a set-top box, a home automation control panel, a security control panel, a TV box (such as SAMSUNG HOMESYNC, APPLETV, or GOOGLE TV), a smart speaker or speaker with an integrated digital assistant (such as SAMSUNG GALAXY HOME, APPLE HOMEPOD, or AMAZON ECHO), a gaming console (such as an XBOX, PLAYSTATION, or NINTENDO), an electronic dictionary, an electronic key, a camcorder, or an electronic picture frame. Still other examples of an electronic device include at least one of various medical devices (such as diverse portable medical measuring devices (like a blood sugar measuring device, a heartbeat measuring device, or a body temperature measuring device), a magnetic resource angiography (MRA) device, a magnetic resource imaging (MRI) device, a computed tomography (CT) device, an imaging device, or an ultrasonic device), a navigation device, a global positioning system (GPS) receiver, an event data recorder (EDR), a flight data recorder (FDR), an automotive infotainment device, a sailing electronic device (such as a sailing navigation device or a gyro compass), avionics, security devices, vehicular head units, industrial or home robots, automatic teller machines (ATMs), point of sales (POS) devices, or Internet of Things (IOT) devices (such as a bulb, various sensors, electric or gas meter, sprinkler, fire alarm, thermostat, street light, toaster, fitness equipment, hot water tank, heater, or boiler). Other examples of an electronic device include at least one part of a piece of furniture or building/structure, an electronic board, an electronic signature receiving device, a projector, or various measurement devices (such as devices for measuring water, electricity, gas, or electromagnetic waves). Note that, according to various embodiments of this disclosure, an electronic device may be one or a combination of the above-listed devices. According to some embodiments of this disclosure, the electronic device may be a flexible electronic device. The electronic device disclosed here is not limited to the above-listed devices and may include new electronic devices depending on the development of technology.

In the following description, electronic devices are described with reference to the accompanying drawings, according to various embodiments of this disclosure. As used here, the term “user” may denote a human or another device (such as an artificial intelligent electronic device) using the electronic device.

Definitions for other certain words and phrases may be provided throughout this patent document. Those of ordinary skill in the art should understand that in many if not most instances, such definitions apply to prior as well as future uses of such defined words and phrases.

None of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) unless the exact words “means for” are followed by a participle. Use of any other term, including without limitation “mechanism,” “module,” “device,” “unit,” “component,” “element,” “member,” “apparatus,” “machine,” “system,” “processor,” or “controller,” within a claim is understood by the Applicant to refer to structures known to those skilled in the relevant art and is not intended to invoke 35 U.S.C. § 112(f).

, discussed below, and the various embodiments of this disclosure are described with reference to the accompanying drawings. However, it should be appreciated that this disclosure is not limited to these embodiments and all changes and/or equivalents or replacements thereto also belong to the scope of this disclosure.

As discussed above, object navigation is a task in which an embodied agent must navigate to a specific goal object within an unknown environment. This task can be fundamental to other navigation-based embodied tasks because it enables the agent to interact with the goal object. Such object navigation tasks usually require large-scale training in visual environments with labeled objects.

While conventional techniques for object navigation achieve good results when trained on specific datasets with limited goal objects and similar environments, they may perform poorly when faced with novel objects or environments due to distribution shifts. Real-world situations often involve diverse objects and varied environments, making it difficult and costly to collect extensive, annotated trajectory data. As a result, generalized zero-shot object navigation, in which the navigation agent can adapt to novel objects and environments without additional training, is a crucial area of study.

To successfully navigate to a goal object, the agent should possess both semantic scene understanding and commonsense reasoning abilities. Semantic scene understanding involves identifying objects present in the environment, while commonsense reasoning involves making logical inferences about the location of the goal object according to the scene understanding. However, current zero-shot (i.e., unseen) object navigation methods have not effectively addressed this requirement and often lack commonsense reasoning abilities. In addition, some methods still require training on other goal-oriented navigation tasks and environments.

In some techniques, knowledge in pre-trained models for semantic scene understanding and commonsense reasoning can be transferred to open-world object navigation without any navigation experience nor any other training on the visual environments to achieve training-free zero-shot object navigation. However, these large pre-trained models may not directly generate navigation actions well. Thus, it would be advantageous to mitigate the gap between the pre-trained knowledge and navigation actions.

This disclosure provides various techniques for zero-shot object navigation using large language models (LLMs). As described in more detail below, the disclosed systems and methods provide a zero-shot object navigation framework that incorporates commonsense knowledge into an exploration method, frontier-based exploration (FBE), using Probabilistic Soft Logic (PSL). PSL is a declarative templating language that defines a special class of Markov random fields using first order logical rules. PSL provides a simple framework to incorporate commonsense knowledge from LLMs into exploration in a zero-shot manner. Unlike conventional techniques that rely on implicit training of commonsense using neural networks, the disclosed embodiments use soft logic predicates to represent knowledge in a continuous value space, which is then assigned to each frontier, enabling more effective exploration. In particular, the framework leverages pre-trained models and can seamlessly generalize to unseen environments and novel object types. Then, the framework utilizes a pre-trained commonsense reasoning language model that takes the room and object information as context to infer the correspondence between the rooms and objects.

Note that while some of the embodiments discussed below are described in the context of use in consumer electronic devices (such as home robots), this is merely one example, and it will be understood that the principles of this disclosure may be implemented in any number of other suitable contexts and may use any suitable devices.

illustrates an example network configurationincluding an electronic device according to this disclosure. The embodiment of the network configurationshown inis for illustration only. Other embodiments of the network configurationcould be used without departing from the scope of this disclosure.

According to embodiments of this disclosure, an electronic deviceis included in the network configuration. The electronic devicecan include at least one of a bus, a processor, a memory, an input/output (I/O) interface, a display, a communication interface, or a sensor. In some embodiments, the electronic devicemay exclude at least one of these components or may add at least one other component. The busincludes a circuit for connecting the components-with one another and for transferring communications (such as control messages and/or data) between the components.

The processorincludes one or more processing devices, such as one or more microprocessors, microcontrollers, digital signal processors (DSPs), application specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs). In some embodiments, the processorincludes one or more of a central processing unit (CPU), an application processor (AP), a communication processor (CP), or a graphics processor unit (GPU). The processoris able to perform control on at least one of the other components of the electronic deviceand/or perform an operation or data processing relating to communication or other functions. As described in more detail below, the processormay perform one or more operations for zero-shot object navigation using large language models.

The memorycan include a volatile and/or non-volatile memory. For example, the memorycan store commands or data related to at least one other component of the electronic device. According to embodiments of this disclosure, the memorycan store software and/or a program. The programincludes, for example, a kernel, middleware, an application programming interface (API), and/or an application program (or “application”). At least a portion of the kernel, middleware, or APImay be denoted an operating system (OS).

The kernelcan control or manage system resources (such as the bus, processor, or memory) used to perform operations or functions implemented in other programs (such as the middleware, API, or application). The kernelprovides an interface that allows the middleware, the API, or the applicationto access the individual components of the electronic deviceto control or manage the system resources. The applicationmay support one or more functions for zero-shot object navigation using large language models as discussed below. These functions can be performed by a single application or by multiple applications that each carry out one or more of these functions. The middlewarecan function as a relay to allow the APIor the applicationto communicate data with the kernel, for instance. A plurality of applicationscan be provided. The middlewareis able to control work requests received from the applications, such as by allocating the priority of using the system resources of the electronic device(like the bus, the processor, or the memory) to at least one of the plurality of applications. The APIis an interface allowing the applicationto control functions provided from the kernelor the middleware. For example, the APIincludes at least one interface or function (such as a command) for filing control, window control, image processing, or text control.

The I/O interfaceserves as an interface that can, for example, transfer commands or data input from a user or other external devices to other component(s) of the electronic device. The I/O interfacecan also output commands or data received from other component(s) of the electronic deviceto the user or the other external device.

The displayincludes, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a quantum-dot light emitting diode (QLED) display, a microelectromechanical systems (MEMS) display, or an electronic paper display. The displaycan also be a depth-aware display, such as a multi-focal display. The displayis able to display, for example, various contents (such as text, images, videos, icons, or symbols) to the user. The displaycan include a touchscreen and may receive, for example, a touch, gesture, proximity, or hovering input using an electronic pen or a body portion of the user.

The communication interface, for example, is able to set up communication between the electronic deviceand an external electronic device (such as a first electronic device, a second electronic device, or a server). For example, the communication interfacecan be connected with a networkorthrough wireless or wired communication to communicate with the external electronic device. The communication interfacecan be a wired or wireless transceiver or any other component for transmitting and receiving signals.

The wireless communication is able to use at least one of, for example, WiFi, long term evolution (LTE), long term evolution-advanced (LTE-A), 5th generation wireless system (5G), millimeter-wave or 60 GHz wireless communication, Wireless USB, code division multiple access (CDMA), wideband code division multiple access (WCDMA), universal mobile telecommunication system (UMTS), wireless broadband (WiBro), or global system for mobile communication (GSM), as a communication protocol. The wired connection can include, for example, at least one of a universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). The networkorincludes at least one communication network, such as a computer network (like a local area network (LAN) or wide area network (WAN)), Internet, or a telephone network.

The electronic devicefurther includes one or more sensorsthat can meter a physical quantity or detect an activation state of the electronic deviceand convert metered or detected information into an electrical signal. For example, one or more sensorscan include one or more cameras or other imaging sensors for capturing images of scenes. The sensor(s)can also include one or more buttons for touch input, a gesture sensor, a gyroscope or gyro sensor, an air pressure sensor, a magnetic sensor or magnetometer, an acceleration sensor or accelerometer, a grip sensor, a proximity sensor, a color sensor (such as a red green blue (RGB) sensor), a bio-physical sensor, a temperature sensor, a humidity sensor, an illumination sensor, an ultraviolet (UV) sensor, an electromyography (EMG) sensor, an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, an infrared (IR) sensor, an ultrasound sensor, an iris sensor, or a fingerprint sensor. The sensor(s)can further include an inertial measurement unit, which can include one or more accelerometers, gyroscopes, and other components. In addition, the sensor(s)can include a control circuit for controlling at least one of the sensors included here. Any of these sensor(s)can be located within the electronic device.

In some embodiments, the electronic devicecan be a wearable device or an electronic device-mountable wearable device (such as an HMD). For example, the electronic devicemay represent an AR wearable device, such as a headset with a display panel or smart eyeglasses. In other embodiments, the first external electronic deviceor the second external electronic devicecan be a wearable device or an electronic device-mountable wearable device (such as an HMD). In those other embodiments, when the electronic deviceis mounted in the electronic device(such as the HMD), the electronic devicecan communicate with the electronic devicethrough the communication interface. The electronic devicecan be directly connected with the electronic deviceto communicate with the electronic devicewithout involving a separate network.

The first and second external electronic devicesandand the servereach can be a device of the same or a different type from the electronic device. According to certain embodiments of this disclosure, the serverincludes a group of one or more servers. Also, according to certain embodiments of this disclosure, all or some of the operations executed on the electronic devicecan be executed on another or multiple other electronic devices (such as the electronic devicesandor server). Further, according to certain embodiments of this disclosure, when the electronic deviceshould perform some function or service automatically or at a request, the electronic device, instead of executing the function or service on its own or additionally, can request another device (such as electronic devicesandor server) to perform at least some functions associated therewith. The other electronic device (such as electronic devicesandor server) is able to execute the requested functions or additional functions and transfer a result of the execution to the electronic device. The electronic devicecan provide a requested function or service by processing the received result as it is or additionally. To that end, a cloud computing, distributed computing, or client-server computing technique may be used, for example. Whileshows that the electronic deviceincludes the communication interfaceto communicate with the external electronic deviceor servervia the networkor, the electronic devicemay be independently operated without a separate communication function according to some embodiments of this disclosure.

The servercan include the same or similar components-as the electronic device(or a suitable subset thereof). The servercan support to drive the electronic deviceby performing at least one of operations (or functions) implemented on the electronic device. For example, the servercan include a processing module or processor that may support the processorimplemented in the electronic device. As described in more detail below, the servermay perform one or more operations to support techniques for zero-shot object navigation using large language models.

Althoughillustrates one example of a network configurationincluding an electronic device, various changes may be made to. For example, the network configurationcould include any number of each component in any suitable arrangement. In general, computing and communication systems come in a wide variety of configurations, anddoes not limit the scope of this disclosure to any particular configuration. Also, whileillustrates one operational environment in which various features disclosed in this patent document can be used, these features could be used in any other suitable system.

illustrates an example frameworkfor zero-shot object navigation using large language models according to this disclosure. For ease of explanation, the frameworkis described as being implemented using one or more components of the network configurationofdescribed above, such as the electronic device. However, this is merely one example, and the frameworkcould be implemented using any other suitable device(s) (such as the server) and in any other suitable system(s).

As described in greater detail below, the electronic deviceuses the frameworkto perform semantic scene understanding and commonsense reasoning via one or more large pre-trained models in a zero-shot manner. The electronic devicealso combines frontier based exploration techniques with commonsense reasoning via PSL. To promote understanding of the framework, it may be helpful to further describe zero-shot object navigation and PSL as used in conjunction with the framework.

Zero-Shot Object Navigation

In an object navigation task, an agent may be placed randomly in an unseen environment.illustrates an example environmentin which an agentperforms object navigation according to this disclosure. Here, the agentis a robot, although the agent could be another electronic device capable of exploring an environment. The agentis given a goal object category (e.g., chair, fireplace, cabinet, etc.), and the objective is to navigate to any object instance that belongs to the category. At each step t, the agenthas an observation including an image and sometimes pose readings.illustrates an example imagecaptured by the agentduring object navigation according to this disclosure. Based on the observation (including the image), the agentcan choose an action in the action space, which can include a ‘STOP’ action to terminate the navigation process. The navigation is considered successful if the agentstops within a threshold distance of the goal object and the object is visible without further moving.

As noted earlier, navigation learning in the real world is not practical with its high cost, and most current methods are hard to generalize to new environments and new objects. Thus, the zero-shot navigation used in the frameworkincludes three levels. The first level is task-level, in which the agentcan perform object goal navigation without object goal navigation training. The second level is environment-level, which means, for a new set of environments, the model allows the agentto perform object navigation in the set of environments without training on any data from the environments. The third level is object-level, where the model can generalize to new goal objects without further training.

The frameworkuses principles of frontier-based exploration, which is a heuristic exploration method and can be adapted to object navigation. As shown in, a frontierin the environmentis defined as a border between a free area and an unseen area. A free area is defined as an area that the agenthas seen and is not occupied by an obstacle. Using frontier based exploration, the agentcan choose the closest frontier(with a distance threshold d) as the next sub-goal after it reaches a frontier. The agent can directly navigate to the goal object after it detects one. For example, as in, after detecting a living room in the environment, the agentmay prefer to explore the unseen area in the living room to find a television or sofa.

Probabilistic Soft Logic

PSL is a probabilistic programming language that defines hinge-loss Markov random fields (HL-MRF) using a syntax based on first-order logic. Specifically, PSL models relational dependencies using weighted first-order logical clauses, referred to as rules. For example, for the relation dependency between a frontier near an object and choosing this frontier as a sub-goal, the following rule can be established.

In this rule, the predicate IsCooccur measures the relation between the target object and one of the other objects, IsNearObj measures whether a frontier is near an object, ChooseFrontier (Frontier) is the soft value of choosing a frontier, and w measures the relative importance of this rule in the model.

During PSL inference, observed variables X, IsCooccur, and IsNearObj can be replaced with actual entities and values. This process is referred to as grounding, and each concrete instance of a rule is referred to as a ground rule. PSL defines a hinge-loss potential function for each grounded rule over unobserved variable Y=ChooseFrontier (Frontier), such as by the following.